> On Sep 7, 2018, at 5:39 AM, Yingjie Wu <[email protected]> wrote: > > Thank you very much for your reply. > > I'm a bit confused if I use what you recommend: - snes_fd -pc_type lu means > that I explicitly construct the Jacobian matrix using the finite difference > method, construct the precondition matrix using the completely LU > decomposition, and solve the step size \ delta x with the GMRES method > (default). > > In fact, what I want to use is to approximate the vector product of a matrix > with finite difference, so that the explicit construction of Jacobian > matrices can be avoided. If so, should I use MatrixFreeMethod? How should I > set it up? If I want to set up precondition, what do I need to add? > > In addition, I want to output variables in each nolinear step. What should I > add code to make SNES step by step?
SNESMonitorSet() > There may be many problems, but they bother me very much. I am looking > forward to your reply. > > Thanks, > Yingjie > > > Matthew Knepley <[email protected]> 于2018年9月6日周四 下午10:34写道： > On Thu, Sep 6, 2018 at 4:47 AM Yingjie Wu <[email protected]> wrote: > Dear Petsc developer: > Hi, > Thank you for your previous help. > I recently modeled on PETSc's SNES example and wrote a computer program > myself. This program is mainly for solving nonlinear equations of thermal > hydraulics. > ∇·（λ∇T） - ∇_y（ρ*Cp*u） - T_source = 0 > > w*ρ*u = ρg - ∇_y（P） > > ∇·（ 1/w * ∇P ） = - ∇（ ρg / w ） > Where P, T and u are variables, the distribution represents pressure, > temperature and velocity. The rest are nonlinear physical parameters and > constants. > Because the program is very preliminary, so I use - snes_mf so that I can > save the part of writing to calculate the Jacobian matrix. > After compiling and passing, I found that the residual function had not > dropped to a small enough level, but the program stopped, as follows: > Setting Up: -snes_mf -snes_monitor -draw_pause 10 -snes_view > > First, do not use -snes_mf. It is not for testing, but for sophisticated use. > The first option you > might try is > > -snes_fd -pc_type lu > > That uses a full Jacobian and LU factorization for a direct solve. Always run > the solve using > > -snes_view -snes_converged_reason -snes_monitor -ksp_converged_reason > -ksp_monitor_true_residual > > When that gets too expensive, you can try > > -snes_fd_color -snes_fd_color_use_mat -mat_coloring_type greedy > > but that requires you to preallocate the Jacobian matrix correctly. > > Thanks, > > Matt > 0 SNES Function norm 3.724996516631e+09 > > 1 SNES Function norm 2.194322909557e+09 > > 2 SNES Function norm 1.352051559826e+09 > > 3 SNES Function norm 1.522311916217e+08 > > SNES Object: 1 MPI processes > > type: newtonls > > maximum iterations=50, maximum function evaluations=10000 > > tolerances: relative=1e-08, absolute=1e-50, solution=1e-08 > > total number of linear solver iterations=1298 > > total number of function evaluations=11679 > > norm schedule ALWAYS > > SNESLineSearch Object: 1 MPI processes > > type: bt > > interpolation: cubic > > alpha=1.000000e-04 > > maxstep=1.000000e+08, minlambda=1.000000e-12 > > tolerances: relative=1.000000e-08, absolute=1.000000e-15, > lambda=1.000000e-08 > > maximum iterations=40 > > KSP Object: 1 MPI processes > > type: gmres > > restart=30, using Classical (unmodified) Gram-Schmidt Orthogonalization > with no iterative refinement > > happy breakdown tolerance 1e-30 > > maximum iterations=10000, initial guess is zero > > tolerances: relative=1e-05, absolute=1e-50, divergence=10000. > > left preconditioning > > using PRECONDITIONED norm type for convergence test > > PC Object: 1 MPI processes > > type: none > > linear system matrix = precond matrix: > > Mat Object: 1 MPI processes > > type: mffd > > rows=300, cols=300 > > Matrix-free approximation: > > err=1.49012e-08 (relative error in function evaluation) > > Using wp compute h routine > > Does not compute normU > > I would like to know why the residual function can not continue to decline, > and why the program will stop before convergence. > I do not know much about the convergence criteria and convergence rules of > PETSc for solving nonlinear equations. I hope I can get your help. > I'm looking forward to your reply~ > > Thanks, > Yingjie > > > -- > What most experimenters take for granted before they begin their experiments > is infinitely more interesting than any results to which their experiments > lead. > -- Norbert Wiener > > https://www.cse.buffalo.edu/~knepley/
